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Top-k Query Processing in Uncertain Databases (2007)
| Citations: | 2 - 0 self |
BibTeX
@MISC{Chen07top-kquery,
author = {Kevin Chen and Chuan Chang},
title = {Top-k Query Processing in Uncertain Databases},
year = {2007}
}
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Abstract
Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between score and uncertainty information makes traditional top-k processing techniques inapplicable to uncertain databases. In this paper we introduce new probabilistic formulations for top-k queries. Our formulations are based on marriage of traditional top-k semantics with possible worlds semantics. In the light of these formulations, we construct a framework that encapsulates a novel probabilistic model and efficient query processing techniques to tackle the challenges raised by uncertain data settings. We prove that our techniques minimize the number of accessed tuples, and the number of materialized possible query answers. Our experiments show the efficiency of our techniques under different data distributions with orders of magnitude improvement over naïve materialization of possible worlds. 1
Keyphrases
uncertain database top-k query processing accessed tuples top-k query different data distribution efficient query processing technique materialized possible query answer traditional top-k processing na ve materialization uncertainty information uncertain data setting new probabilistic formulation magnitude improvement novel probabilistic model possible world semantics possible world top-k processing traditional top-k processing technique traditional top-k semantics
